• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.54-63
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• 2006

본 논문에서는 레거시 시스템에 대한 기존 도메인 분석의 문제점을 해결하기 위하여 동적 모델링을 기반으로 하는 확장된 워크플로우 메커니즘을 이용한 도메인 분석 방법론을 제안한다. 이 방법론을 WODA(Werldlow Oriented Domain Analysis)라 명명한다. 제안하는 절차를 통해 공통/비공통 컴포넌트를 식별 및 컴포넌트들의 클러스터를 추출할 수 있다. 이를 통해 새로운 시스템을 개발 시 효율적으로 재사용하고자 한다. 동적 분석으로 특정한 시스템에 발생 가능한 시나리오들을 식별한 후, 제안한 컴포넌트 테스트 플랜 매트릭스를 이용해 재사용성이 높은 컴포넌트와 컴포넌트 시나리오를 결정한다. 또한 컴포넌트 가중치 측정을 통해 재사용 가능한 컴포넌트들의 중요성과 빈도수를 인식하고 컴포넌트 시나리오들의 우선순위를 도출 할 수 있다. 구현한 자동화 모델링 도구인 WODA을 통해 UPS(Uninterrupted Power Supply)에 적용 사례를 소개한다.

• 한국음향학회지
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• 제19권5호
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• pp.65-70
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• 2000

라우드니스(loudness)는 음질 평가에 있어서 가장 중요한 음질 인자로 간주되고 있고, 그 계산을 위해 정상음에 대한 국제규격도 마련되어 있다. 본 연구에서는, 이의 일반화를 위해 라우드니스 계산 모델에 과도음 해석 과정을 포함한 새로운 방법을 제시하고자 한다. 이를 위하여 과도 신호의 대역 분할 및 대역별 음압 레벨 변화 예측을 위한 신호 처리 기법과 과도 음에 대한 청각 반응을 모델링한 포스트 마스킹(post-masking) 및 라우드니스 시간 적분 모델이 도입되었다. 또한 순음의 라우드니스 해석에서 기존 라우드니스 모델이 갖고 있는 신호 해석상의 문제점을 개선하기 위하여 임계 대역폭의 1/2 간격으로 배치된 총 47개의 임계 대역 필터를 이용하였다. 제안된 모델의 유효성을 확인하기 위하여 기존의 임상 실험 결과 비교하였고, 예측치와 임상치는 아주 좋은 일치 경향을 가짐을 확인하였다.

• Smart Structures and Systems
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• 제22권2호
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• pp.167-174
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• 2018

Digital input and output modules are widely used to connect digital sensors and actuators to automation systems. Digital I/O modules provide flexible connectivity extension to numerous sensors and actuators and protect systems from high voltages and currents by isolation. Components in digital I/O modules are inevitably affected by operating and environmental conditions, such as high voltage, high current, high temperature, and temperature cycling. Because digital I/O modules transfer signals or isolate the systems from unexpected voltage and current transients, their failures may result in signal transmission failures and damages to sensitive circuitry leading to system malfunction and system shutdown. In this study, the lifetime of optocouplers, one of the critical components in digital I/O modules, was predicted using Bayesian tracking approaches. Accelerated degradation tests were conducted for collecting the critical performance parameter of optocouplers, current transfer ratio (CTR), during their lifetime. Bayesian tracking approaches, including extended Kalman filter and particle filter, were applied to predict the failure. The performance of each prognostic algorithm was then compared using accuracy and robustness-based performance metrics.

• Smart Structures and Systems
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• 제30권1호
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• 대한수학회보
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• 제29권1호
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• pp.9-13
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• 1992

In this paper, we consider the function related with almost hermitian structure on a copact complex manifold. More precisely, on a 2n-diminsional complex manifold M admitting 2-form .ohm. of rank 2n everywhere, assume that M admits a metric g such that g(JX, JY)=g(X,Y), that is, assume that g defines an hermitian structure on M admitting .ohm. as fundamental 2-form-the 'almost complex structure' J being determined by g and .ohm.:g(X,Y)=.ohm.(X,JY). We consider the function I(g):=.int.$_{M}$ $N^{2}$d $V_{g}$, where N is the norm of Nijenhuis tensor N defined by (J,g). by (J,g).

• 한국수소및신에너지학회논문집
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• 제27권4호
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• pp.349-356
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• 2016

Volumetric capacity metrics at cryogenic condition are critical for technological and commercial development. It must be calculated and reported in a uniform and consistent manner to allow comparisons among different materials. In this paper, we propose a simple and universal protocol for the determination of volumetric capacity of sorbent materials at cryogenic condition. Usually, the sample container volume containing porous sample at RT can be directly determined by a helium expansion test. At cryogenic temperatures, however, this direct helium expansion test results in inaccurate values of the sample container volume for microporous materials due to a significant helium adsorption, resulting significant errors in hydrogen uptake. For reducing this container volume error, therefore, we introduced and applied the indirect method such as 'volume correction using a non-porous material', showing a reliable cold volume correction.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권3호
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• pp.374-380
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• 2015

As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a novel sampling method based on the concept of a confidence ellipse. Results show that the proposed method estimates the SRAM margin metrics in high-sigma regimes more efficiently than the standard Monte Carlo (MC) method.

• 대한임베디드공학회논문지
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• 제10권5호
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• pp.273-279
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• 2015

The vehicle has lots of embedded systems. Each of systems has its own role. In case of the vehicle, simple failure of system can be critical to driver. Therefore all of embedded system should be managed based on importance factors to be effective. In this paper, we consider the resilience as the importance factor for the driving system with ACC(Adaptive Cruise Control). We propose metrics to calculate the resilience of the embedded system. To get the resilience of system, we calculate the reliability and the resilience of nodes in the system using its failure rate. The resilience of whole system can be presented by the resilience of nodes and its weight. We calculate the resilience and compare the centralized structure and the distributed structure.

• 한국IT서비스학회지
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• 제5권1호
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• pp.35-46
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• 2006

In recent years, academic scholars and practitioners have given increasing attention to the importance of strategic performance measurement systems including both financial and non-financial performance metrics. The Balanced Scorecard (BSC) is known as integrated performance management framework that helps an enterprise to translate strategic objectives into relevant performance within an organization. While the current literatures and management articles offer BSC design and implementation. there are few reports of detailed validation of using the rationalized sets of CSF (Critical Success Factors) and KPI (Key Performance Indicators) for the Korean construction industry. This paper first propose the perceived sets of CSF/KPI using current literatures and validate with a major construction company's executives and senior managers in Korea. The paper then examines whether the perceived sets of CSF/KPI have co-relationships with the firm performances. The results of the research contribute in heightening of competitiveness of the Korean construction companies in strategic and performance management.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.